The COVID-19 pandemic is producing massive amounts of information that more often than not lead to different interpretations. The accurate analysis of this daily input of data is crucial to predict possible outcomes and design solutions rapidly. These can only be achieved with expertise in modeling infectious diseases, and with the power of computational science theory and infrastructure. U-M’s Epidemiology Department, in the School of Public Health, has a very strong cohort of researchers who work on mathematically modeling the dynamics of infectious diseases, the analysis of these models, and large scale computer simulations — all to understand the spread and mitigation of pandemics. They are applying their long experience and expertise to the current COVID-19 outbreak, aiding the government make informed decisions, and helping media outlets produce accurate reports for the general public. Marisa Eisenberg, Associate Professor of Epidemiology, of Complex Systems, and of Mathematics, and her colleagues are using a differential equation transmission modeling approach to analyze scenarios and generate short-term forecasts for the COVID-19 epidemic in State of Michigan. They are communicating directly with the Michigan Department of Health and Human Services and providing critical tools, like the Michigan COVID-19 Modeling Dashboard, to inform decision-making. Prof. Eisenberg’s team is helping to forecast the numbers of laboratory-confirmed cases, fatalities, hospitalized patients, and hospital capacity issues (such as ICU beds needed), and examining how social distancing can impact the spread of the epidemic. Prof. Jonathan Zelner, whose research is focused on using spatial and social network analysis and dynamic modeling to prevent infectious diseases, is part of a group helping map the outbreak in Michigan. He also has provided valuable insights to journalists contributing to a better understanding of the situation, including what made New York City so vulnerable to the coronavirus (NYT), the role of wealth inequality during epidemics (CNBC) and what professions and communities are particularly vulnerable to infection (NG).